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Predicting Solar Radiation Using Optimized Generalized Regression Neural Network

In: Application of Machine Learning Models in Agricultural and Meteorological Sciences

Author

Listed:
  • Mohammad Ehteram

    (Semnan University, Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering)

  • Akram Seifi

    (Vali-e-Asr University of Rafsanjan, Department of Water Science and Engineering, College of Agriculture)

  • Fatemeh Barzegari Banadkooki

    (Payame Noor University, Agricultural Department)

Abstract

One of the most important components of the hydrological cycle is solar radiation. Three stations in Iran were used to predict monthly solar radiation (SOR) using the optimized generalized regression neural network (GRNN). The Henry gas solubility optimization (HGSO), antlion optimization (ANO), and salp swarm algorithm (SSA) were used to adjust the parameters of the GRNN. Sunny hours had the highest correlation with SOR at all stations. Furthermore, the GRNN-HGSO model outperformed the other methods. At Mazandaran station, the median of observed data, GRNN-HGSO, GRNN-ANO, GRNN-SSA, and GRNN model was 19 MJ m−2, 19 MJ m−2, 19 MJ m−2, 21 MJ m−2, and 24 MJ m−2, respectively. In this study, soft computing models had a high ability to predict SOR in different climates. Using the models of the current study, decision-makers can identify the regions with the highest SRO. These regions are suitable for the construction of power plants.

Suggested Citation

  • Mohammad Ehteram & Akram Seifi & Fatemeh Barzegari Banadkooki, 2023. "Predicting Solar Radiation Using Optimized Generalized Regression Neural Network," Springer Books, in: Application of Machine Learning Models in Agricultural and Meteorological Sciences, chapter 0, pages 163-174, Springer.
    • Handle: RePEc:spr:sprchp:978-981-19-9733-4_16
    DOI: 10.1007/978-981-19-9733-4_16
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